HPE involves two sub-tasks: location (determining where the keypoints are) and classification (determining which kinds the keypoints are). The location needs plenty of local details to get pixel-level accuracy. While classification requires a relatively larger receptive field to extract discriminative semantic representations [1]}. Consequently, HPE methods have to fuse multi-scale information to make a balance between these two sub-tasks [2]}. Most nowadays HPE methods [3]}, [4]}, [5]}, [6]}, [7]} repeatedly downscale feature maps to enlarge the receptive fields. Feature maps of different spatial sizes (i.e. \(1/4\) , \(1/8\) , \(1/16\) , and \(1/32\)  of the input image) are then resized and summed to exploit multi-scale information.
<FIGURE>